Diatom-based estimation of sea surface salinity in the south Baltic Sea and Kattegat

Pub Date : 2014-02-20 DOI:10.5200/BALTICA.2014.27.22
B. Kotrys, M. Tomczak, A. Witkowski, J. Harff, J. Seidler
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引用次数: 3

Abstract

The new diatom-based sea-surface salinity (SSS) estimation has been applied to a collection of 27 taxa in 48 present-day sediment and surface water samples recovered in the Baltic Sea and Kattegat. The sediment core 303610-12 (2005) from the Eastern Gotland was chosen for the study of the Holocene sequence spanning the past 8160 yrs BP. The Artificial Neuronal Network (ANN) method provided an estimation of spring (March- April) SSS values ranging between 7.04-8.25 ‰. The low amplitude of salinity change might be caused by mix- ing of fresh water with upper surface layer of the Baltic Sea due to high precipitation and riverine input. These findings were compared with independent geochemical proxies for salinity (K, Ti and S) derived from XRF Core Scanner record. Significant correlation between salinity and sulphur records and an inverse correlation between K and Ti demonstrate that the ANN method, when combined with quantitative and qualitative analyses of diatoms, provides a useful tool for palaeo-salinity reconstructions from the Holocene sediments of the Baltic Sea.
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基于硅藻的南波罗的海和卡特加特海表盐度估算
新的基于硅藻的海面盐度(SSS)估计已应用于在波罗的海和卡特加特恢复的48个现代沉积物和地表水样品中的27个分类群。选取东哥特兰岛303610-12(2005)沉积物岩心对8160年BP的全新世序列进行了研究。人工神经网络(ANN)方法提供了春季(3 - 4月)SSS值在7.04-8.25‰之间的估计。盐度变化的低幅值可能是由于高降水和河流输入导致淡水与波罗的海上层混合造成的。这些发现与XRF岩心扫描仪记录的盐度(K、Ti和S)的独立地球化学指标进行了比较。盐度和硫的显著相关性以及K和Ti的负相关性表明,人工神经网络方法与硅藻的定量和定性分析相结合,为波罗的海全新世沉积物的古盐度重建提供了有用的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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